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Efficiency of Covariance Matrix Estimators for Maximum Likelihood Estimation

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  • Porter,Jack

Abstract

When econometric models are estimated by maximum likelihood, the conditional information matrix variance estimator is usually avoided in choosing a method for estimating the variance of the parameter estimate. However, the conditional information matrix estimator attains the semiparametric efficiency bound for the variance estimation problem. Unfortunately, for even moderately complex models, the integral involved in computation of the conditional information matrix estimator is prohibitively difficult to solve. Simulation is suggested to approximate the integral, and two simulation variance estimators are proposed. Monte Carlo results suggest these estimators are attractive in providing accurate confidence interval coverage rates compared to the standard maximum likelihood variance estimators.

Suggested Citation

  • Porter,Jack, 2002. "Efficiency of Covariance Matrix Estimators for Maximum Likelihood Estimation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 431-440, July.
    • Handle: RePEc:bes:jnlbes:v:20:y:2002:i:3:p:431-40
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    Cited by:

    1. Kenney Toby & Gu Hong, 2012. "Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-46, September.
    2. Fernández-Val, Iván & Vella, Francis, 2011. "Bias corrections for two-step fixed effects panel data estimators," Journal of Econometrics, Elsevier, vol. 163(2), pages 144-162, August.
    3. Ivan Fernandez-Val, 2005. "Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects," Boston University - Department of Economics - Working Papers Series WP2005-38, Boston University - Department of Economics.
    4. Fernández-Val, Iván, 2009. "Fixed effects estimation of structural parameters and marginal effects in panel probit models," Journal of Econometrics, Elsevier, vol. 150(1), pages 71-85, May.

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